The controlnet-canny model has various applications in the field of image processing and computer vision. It could be used in tasks such as image segmentation, where the accurate identification of edges is crucial. Additionally, it could be used for image editing and enhancement, allowing users to selectively highlight and emphasize edges in their images. This model could also be used as a tool to generate artistic effects, by altering the strength and appearance of edges in a given image. Possible products or practical uses of this model could be an image editing software that offers advanced edge manipulation capabilities, or a computer vision algorithm that relies on accurate edge detection for tasks like object recognition or feature extraction.
- Cost per run
- Avg run time
- Nvidia A100 (40GB) GPU
|Stable Diffusion Depth2img||$0.0322||50,528|
You can use this area to play around with demo applications that incorporate the Controlnet Canny model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Model Name||Controlnet Canny|
Modify images using canny edge detection
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||No paper link provided|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$0.0322|
|Prediction Hardware||Nvidia A100 (40GB) GPU|
|Average Completion Time||14 seconds|